Contents
- Pricing Overview
- Specifications
- CoreWeave B200 Deployment
- Provider Comparison
- FAQ
- Related Resources
- Sources
Pricing Overview
CoreWeave B200: 8x clusters only. $68.80/hr ($8.60 per GPU). Requires minimum commitments.
Costs more than H200 ($6.30/GPU in 8x clusters), less than AWS. Good for large-scale training where throughput matters.
Specifications
B200: 192GB HBM3e, 8.0TB/s bandwidth. 16,896 CUDA cores, 5,632 Tensor cores. Blackwell, transformer-optimized.
Key B200 capabilities:
- 16,896 CUDA cores
- 5,632 Tensor cores
- 192GB HBM3e memory
- 8.0TB/s memory bandwidth
- FP32: ~75 TFLOPS
- TF32: ~2,200 TFLOPS (2.2 PFLOPS sparse)
- Blackwell architecture with MMA units
192GB per GPU clears memory bottlenecks in LLM training. Lower power-per-TFLOP than H100. 8xB200 clusters crush training and inference on 100B+ parameter models.
CoreWeave B200 Deployment
CoreWeave provisions B200 clusters within hours for qualified customers. The platform offers both on-demand and reserved capacity options. Deployment includes networking configuration, shared filesystem setup, and Docker container support.
Customers provide container images, training scripts, and dataset specifications. CoreWeave handles hardware orchestration, monitoring, and billing. The platform supports standard distributed training frameworks including PyTorch Distributed and Megatron-LM.
Persistent storage integrates with cluster provisioning at $0.30 per GB monthly. Teams typically allocate storage for model checkpoints, training data, and logs. An 8-GPU cluster with checkpointing every 500 steps requires approximately 1TB of persistent storage for multi-week training runs.
Provider Comparison
CoreWeave's B200 pricing emphasizes premium on-demand capacity with minimal wait times. Lambda Labs and RunPod do not yet offer B200 to standard customers as of March 2026. CoreWeave's cluster-only model differs from RunPod's flexible per-GPU rental, targeting teams with larger budgets and predetermined workload specifications.
For comparison, CoreWeave's H200 8-GPU cluster costs $50.44 per hour ($6.30 per GPU). The B200 cluster adds roughly $18.36 per hour or 36% premium. The performance improvement in transformer operations justifies this cost for frontier model development.
FAQ
What workloads benefit most from B200 clusters? B200 excels in large language model training, particularly foundation models exceeding 100 billion parameters. Multimodal models and code generation systems show the strongest performance improvements. Teams training models under 70 billion parameters may find H200 or A100 more cost-effective.
Can I rent a subset of an 8-GPU B200 cluster? CoreWeave does not offer single B200 GPU rental. Minimum cluster order involves full 8-GPU provisioning and commitment to the cluster rental period.
What is the total cost for a month of continuous B200 training? An 8-GPU B200 cluster runs $68.80 per hour. Continuous monthly operation costs approximately $49,536 (730 hours). Many teams reduce expenses through spot instances or partial-month provisioning when available.
How does B200 memory bandwidth impact training speed? B200's 8.0TB/s bandwidth represents roughly 67% improvement over H200's 4.8TB/s. This impacts batch size capacity and reduces gradient communication overhead in distributed training. Transformer models with sequence lengths exceeding 16,384 tokens see the most significant speedup.
Is CoreWeave B200 suitable for fine-tuning existing models? While technically suitable, B200 represents significant cost per task. Fine-tuning projects typically show better ROI on A100, A10, or L40S GPUs. B200 justifies its cost primarily for pretraining and foundation model development.
Related Resources
Compare B200 with H200 specifications and CoreWeave GPU pricing. Explore pricing across alternatives including Lambda GPU pricing and RunPod GPU pricing. Learn about H100 performance for cost-benefit analysis.